The advancement of Artificial Intelligence technologies has paved the way for the increasing use of autonomous systems in various fields, including air, land, and sea. Maritime is an important domain for applying autonomous systems, as collisions at sea can result in significant losses. How- ever, the development of autonomous systems in the maritime domain involves the collection of vast amounts of data from sensors on vehicles, which poses significant challenges in understanding the types of data collected from vehicle sensors and the corresponding data system requirements due to the large amount of data collected. To address this issue, this thesis proposes the use of Oper- ational Design Domain (ODD), inspired by the autonomous car domain, to structure and manage the data. An ODD taxonomy was designed with the help of BSI PAS 1883 and ASAM OpenODD concepts. This thesis presents an initial draft of the ODD taxonomy for the maritime domain, followed by the implementation of ODD samples on actual data. The work involved converting unstructured data into ODD-friendly data. The results of this thesis include an ODD taxonomy, a framework, and a use case demonstrating the application of these concepts to actual data. These results provide an example of how to manage large amounts of data in the autonomous maritime environment and facilitate further testing of autonomous systems in this domain.